Image Segmentation Software for the Extraction of Single-cell Data in S. cerevisiae

Natalie Ostroff, University of California, San Diego

Photo of Natalie Ostroff

The yeast Saccharomyces cerevisiae has become an essential model for the study of fundamental aspects of eukaryotic cell behavior. As research in Systems Biology moves steadily toward the development of a quantitative description of biological networks, there is an increasing need for a yeast specific single-cell assay capable of capturing the dynamics of cellular processes. Recent advances in microscopy and microfluidic devices promise to facilitate the acquisition of long image sequences monitoring large populations of cells. However, manual extraction of single-cell information from these images is prohibitively time consuming. Here, we present a software package for the automated extraction of single-cell trajectories from a series of fluorescence microscopy images. Operated in automatic mode, image segmentation is over 95% accurate, and a manual correction option allows the user to correct any errors. Coupled with advanced imaging technologies, the availability of this software should greatly aid quantitative modelers of both native and synthetic gene circuits by facilitating the long-term observance of dynamical properties of gene regulation in S. cerevisiae.

Abstract Author(s): Scott Cookson, Natalie Ostroff, Wyming Lee Pang, Dmitri Volfson, & Jeff Hasty